Oraclaw Decision Intelligence
About
12 MCP tools with 19 ML algorithms for AI agents — bandits, solvers, forecasters, risk models. All under 25ms, deterministic.
Details
- Author
- Whatsonyourmind
- Downloads
- 112
- Categories
- Other, AI
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Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Oraclaw Decision IntelligenceCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
{
"mcpServers": {
"oraclaw": {
"command": "npx",
"args": [
"@oraclaw/mcp-server"
]
}
}
}
optimize_bandit
Select the next option to try from 2+ variants that each have observed pull/reward history, balancing exploitation against exploration (UCB1, Thompson sampling, or epsilon-greedy). Use when you must pick one arm now from A/B test variants, ad/email/copy options, or ranked recommendations and have past trial counts. Returns the chosen arm plus exploitation score, exploration bonus, and a regret estimate. For per-call context features use optimize_contextual; for continuous parameters use optimize_cmaes.
optimize_contextual
Select the best option given a numeric context/feature vector, using a LinUCB contextual bandit that learns per-context preferences from optional history. Use when the best choice changes with situational features that vary call-to-call (user/segment attributes, time of day, current regime). Returns the chosen arm with its LinUCB expected reward and confidence width. If you have no per-call features, use optimize_bandit.
optimize_cmaes
[Premium] Optimize N continuous parameters against a weighted-sum objective using CMA-ES, suited to non-convex/noisy/gradient-free landscapes. Use for hyperparameter search, simulator calibration, or control-policy tuning where you supply per-dimension objective weights. Returns the best parameter vector, its objective value, iteration/evaluation counts, and a converged flag; stochastic init means repeated runs may differ. Use optimize_evolve for discrete spaces and solve_constraints for linear/MIP constraints. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).
solve_constraints
[Premium] Solve a linear / mixed-integer / quadratic program with the HiGHS solver and return a provably optimal assignment. Use when your objective and constraints are linear (or quadratic) over named continuous/integer/binary variables: budget allocation, supply or capacity planning with integer counts, allocation with hard caps. Returns solver status (optimal/infeasible/unbounded), the objective value, and the solved value per variable. Use optimize_cmaes for black-box objectives and solve_schedule for task-to-slot assignment. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).
solve_schedule
Assign tasks to time slots to maximize total score by matching each task's energy requirement to a slot's energy level (and respecting duration). Use for deep-work blocking, shift or session planning, or any task-to-slot fit where high-energy work should land in high-energy slots. Returns the assignments, any unassigned task IDs, and a total score. For arbitrary linear constraints use solve_constraints; for routing use plan_pathfind.
analyze_graph
[Premium] Compute structural metrics of a directed weighted graph: PageRank centrality, Louvain community clusters, an optional critical path between two given nodes, and bottleneck nodes. Use to find the most influential nodes, cluster a dependency/knowledge graph, or locate chokepoints in supply or process networks. Returns per-node PageRank and community index, cluster summaries, the critical path with its weight, and bottlenecks. For a single source-to-goal route, use plan_pathfind (free). Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).
analyze_risk
[Premium] Compute portfolio Value-at-Risk and Conditional VaR (Expected Shortfall) from a historical [asset][time] return matrix and portfolio weights, accounting for cross-asset correlation. Use to size downside risk on a weighted multi-asset book, attribute risk, or run drawdown scenarios with auditable inputs. Returns VaR and CVaR (loss as a positive number) at the requested confidence, plus expected return, volatility, and the horizon used. To sample outcomes from a parametric distribution instead, use simulate_montecarlo. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).
score_convergence
Score how strongly multiple independent sources agree on a single event's probability, using Hellinger-distance agreement plus penalties for dispersion/uncertainty and a freshness weight (recency, source volume, and confidence). Use to fuse 0..1 estimates from polls, prediction markets, or model outputs into one number. Returns a 0..1 convergence score, the volume-weighted consensus probability, source count, and component breakdown. To combine N point predictions instead, use predict_ensemble.
predict_forecast
[Premium] Forecast the next N values of one evenly-spaced numeric time series using ARIMA (non-seasonal trend) or Holt-Winters (additive seasonal, set seasonLength). Use for short-to-medium horizon point forecasts of demand, KPIs, or capacity. Returns the point forecast array plus lower/upper confidence bands and the fitted model description. ARIMA requires at least 20 observations; Holt-Winters needs at least 2 x seasonLength. To flag outliers instead of projecting, use detect_anomaly. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).
detect_anomaly
[Premium] Flag outlier points in a numeric series using a Z-score test (parametric, assumes near-normal) or IQR test (robust to skew/heavy tails). Use for metric monitoring, fraud/abuse signals, sensor noise, or quality control. Returns each anomaly's index, value, and score, plus the underlying statistics (mean/stdDev/threshold for Z-score; q1/q3/IQR/bounds for IQR) and an anomaly count. To project a series forward instead, use predict_forecast. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).
plan_pathfind
Find the shortest path (or k-shortest paths) between a start and end node in a weighted directed graph using A* with selectable heuristic (zero=Dijkstra, time, cost, risk, weighted) and Yen's algorithm for alternatives. Use for routing, dependency resolution, or 'how do I get from X to Y' over a graph; set kPaths>1 for alternatives. Returns the path node IDs, total cost, a time/cost/risk breakdown, nodes explored, and a found flag. For centrality/communities use analyze_graph; for task-to-slot assignment use solve_schedule.
simulate_montecarlo
Draw N samples from one parametric distribution (normal, lognormal, uniform, triangular, beta, or exponential) and summarize the resulting spread. Use to quantify uncertainty around a single random factor: an NPV under an uncertain growth rate, a latency tail, or a reserve estimate. Returns the mean, standard deviation, p5/p25/p50/p75/p95 percentiles, a histogram, and the iteration count; each call re-samples (non-deterministic) and is capped at 2000 iterations. For correlated multi-asset risk, use analyze_risk.
score_calibration
Measure how well a set of probability predictions matched observed binary outcomes, returning the Brier score and log score (lower is better). Use to evaluate a forecaster's or model's calibration: predictions[i] is the probability assigned to event i and outcomes[i] is 1 if it occurred, else 0 (arrays must be equal length). Returns brier_score, log_score, the number of predictions, and the mean predicted vs mean observed rate. To measure agreement across multiple sources instead, use score_convergence.
predict_bayesian
Update a prior probability with weighted evidence signals using a Beta posterior (the prior seeds Beta(prior*10, (1-prior)*10)). Use for incremental belief revision: start from a baseline probability and fold in signals, each a value in [0,1] with a weight, to get a revised posterior. Returns the updated posterior, the prior, per-factor contributions, posterior mean and variance, and a sharpness/calibration score. To combine N independent point predictions use predict_ensemble; to sample a full distribution use simulate_montecarlo.
predict_ensemble
Combine 2+ model point predictions into one consensus using weighted voting, stacking, or Bayesian model averaging, weighting each model by its confidence or supplied historicalAccuracy. Use to fuse heterogeneous predictors (statistical, ML, and human forecasters) into a single number with an uncertainty estimate. Returns the consensus value and confidence, per-model weight share, Shannon entropy of the weights, a cross-model agreement score, epistemic/aleatoric/total uncertainty with a confidence interval, and per-model contributions. To score agreement on a single event probability instead, use score_convergence.
optimize_evolve
Run a genetic algorithm over a fixed-length gene vector (binary, integer, real, or permutation bounds) against a weighted-sum fitness, with an optional Pareto frontier for multi-objective runs. Use for discrete or mixed search spaces (feature selection, integer allocation, permutation/TSP-style problems) or when you want several non-dominated solutions. Returns the best chromosome and fitness, the Pareto frontier when applicable, the convergence generation, total generations, and recent fitness history; results vary run to run (stochastic). For smooth continuous objectives, use optimize_cmaes.
simulate_scenario
Compare named what-if scenarios against a base case where the outcome metric is the sum of the input variables, and rank which variables swing the outcome most. Use for budget sensitivity, deal/forecast what-ifs, or capacity planning across demand assumptions: define a base case of variable=value, then scenarios that override a subset. Returns the base outcome, each scenario's outcome with absolute and percent delta and per-variable changes, plus a sensitivity ranking by total absolute swing. For random sampling from a distribution, use simulate_montecarlo.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"oraclaw decision intelligence": {
"oraclaw": {
"command": "npx",
"args": [
"@oraclaw/mcp-server"
]
}
}
}
}McpServers
{
"oraclaw": {
"command": "npx",
"args": [
"@oraclaw/mcp-server"
]
}
}12 MCP tools with 19 ML algorithms for AI agents — bandits, solvers, forecasters, risk models. All under 25ms, deterministic.
MCP Optimization Tools for AI Agents-- 17 tools (11 free, no key), sub-25ms. Zero LLM cost.
Your AI agent can't do math. OraClaw gives it deterministic optimization, simulation, forecasting, and risk analysis through the Model Context Protocol. Every tool returns structured JSON, runs in under 25ms, and costs nothing to compute.
🚀 Using OraClaw in production — or want managed hosting, premium tools, or priority support?Tell me about your use case →— I read every one.
💬Building something with it?Star the repo and say hi inDiscussions— what you build steers what I ship next.
LLMs generate plausible text, not mathematically optimal answers. OraClaw gives an AI agent a set of deterministic numerical tools it can call instead of guessing — each returns structured JSON from a real algorithm, with no token spend on reasoning. Concretely:
- Your agent needs to pick the next variant to try(A/B test arm, ad/email copy, recommendation) and balance exploration against exploitation — without hand-rolling a bandit or letting the model eyeball it. Calloptimize_bandit(oroptimize_contextualwhen the best choice depends on per-call features).
- Your agent needs a provably optimal allocation or schedule under hard constraints(budget split, integer counts, capacity caps) — without the model hallucinating constraints. Callsolve_constraints(LP/MIP/QP via HiGHS) orsolve_schedulefor task-to-slot fitting.
- Your agent needs to quantify uncertaintyaround an outcome — project a value under an uncertain input, or measure VaR/CVaR on a weighted multi-asset book with auditable assumptions — without a Monte Carlo loop in the prompt. Callsimulate_montecarlo,simulate_scenario, oranalyze_risk.
- Your agent needs a point forecast or an outlier flagon a time series (demand, KPIs, sensor/metric streams) — without inventing trend math. Callpredict_forecast(ARIMA / Holt-Winters) ordetect_anomaly(Z-score / IQR).
- Your agent needs to fuse or score probability signals— combine model outputs, measure how much independent sources agree, or check whether past predictions were well-calibrated. Callpredict_ensemble,score_convergence, orscore_calibration.
- Your agent needs to reason over a graph— rank influential nodes, cluster a dependency/knowledge graph, find a critical path, or route between two nodes. Callanalyze_graphorplan_pathfind.
OraClaw's algorithms have informed implementations in several open-source projects -- through contributed routing specs, algorithm guidance, and shared math -- spanning AI agent orchestration, time-series tracking, vector search, and optimization.
Selected contributions(seeCHANGELOG.mdfor the full list):
- chernistry/bernstein-- agent orchestration framework. LinUCB contextual router (α=0.3) with shadow-evaluation path and interpretable decision reasons, shipped incodex/issue-367-linucb-routerafter a contributed spec correction.
- stxkxs/nanohype-- contextual bandit routing, pluggable strategy registry (hash / sliding-TTL / semantic), cost anomaly detection."Your input shaped a lot of what actually shipped."
- rfivesix/hypertrack-- Bayesian/Kalman-style adaptive estimator with phase-aware ramp. Shipped in 0.8.0-beta.
- AlanHuang99/pyrollmatch-- entropy balancing (Hainmueller 2012) with moment constraints +max_weightcap. Shipped in v0.1.3.
- stffns/vstash-- IDF-sigmoid relevance weighting. Shipped in v0.17.0.
- ✓punkpeye/awesome-mcp-servers-- merged
- ✓TensorBlock/awesome-mcp-servers-- merged
- ✓ MCP Registry, Glama (score A/A/B), PulseMCP, toolsdk-ai -- listed
1. MCP Server (recommended for AI agents)
Add to yourclaude_desktop_config.json:
{ "mcpServers": { "oraclaw": { "command": "npx", "args": ["-y", "@oraclaw/mcp-server"] } } }
"I have 3 email subject line variants. Which should I send next?"
The agent callsoptimize_banditand gets a statistically optimal selection in 0.01ms.
curl -X POST https://oraclaw-api.onrender.com/api/v1/optimize/bandit \ -H 'Content-Type: application/json' \ -d '{ "arms": [ {"id": "A", "name": "Option A", "pulls": 10, "totalReward": 7}, {"id": "B", "name": "Option B", "pulls": 10, "totalReward": 5}, {"id": "C", "name": "Option C", "pulls": 2, "totalReward": 1.8} ], "algorithm": "ucb1" }'
{ "selected": { "id": "C", "name": "Option C" }, "score": 1.876, "algorithm": "ucb1", "exploitation": 0.9, "exploration": 0.976, "regret": 0.1 }
Free tier: 25 calls/day, no API key needed.
import { OraBandit } from '@oraclaw/bandit'; const client = new OraBandit({ baseUrl: 'https://oraclaw-api.onrender.com' }); const result = await client.optimize({ arms: [ { id: 'A', name: 'Short Subject', pulls: 500, totalReward: 175 }, { id: 'B', name: 'Long Subject', pulls: 300, totalReward: 126 }, ], algorithm: 'ucb1', });
14 SDK packages:@oraclaw/bandit,@oraclaw/solver,@oraclaw/simulate,@oraclaw/risk,@oraclaw/forecast,@oraclaw/anomaly,@oraclaw/graph,@oraclaw/bayesian,@oraclaw/ensemble,@oraclaw/calibrate,@oraclaw/evolve,@oraclaw/pathfind,@oraclaw/cmaes,@oraclaw/decide
LLMs generate plausible text, not optimal solutions. Ask GPT to pick the best A/B test variant and it applies a heuristic that ignores the exploration-exploitation tradeoff. Ask it to solve a linear program and it hallucinates constraints. OraClaw gives your agent access to real algorithms -- bandits, solvers, forecasters, risk models -- that return mathematically correct answers in sub-millisecond time, without burning tokens on reasoning.
Free tier (11 tools, no API key — 25 calls/day per IP):
Premium tier (6 tools, requiresORACLAW_API_KEY):
14 of 18 REST endpoints respond in under 1ms. All under 25ms.
# Bayesian inference curl -X POST https://oraclaw-api.onrender.com/api/v1/predict/bayesian \ -H 'Content-Type: application/json' \ -d '{"prior": 0.3, "evidence": [{"factor": "positive_test", "weight": 0.9, "value": 0.05}]}' # Monte Carlo simulation curl -X POST https://oraclaw-api.onrender.com/api/v1/simulate/montecarlo \ -H 'Content-Type: application/json' \ -d '{"simulations": 1000, "distribution": "normal", "params": {"mean": 100, "stddev": 15}}' # Monte Carlo with a non-normal distribution curl -X POST https://oraclaw-api.onrender.com/api/v1/simulate/montecarlo \ -H 'Content-Type: application/json' \ -d '{"simulations": 1000, "distribution": "triangular", "params": {"min": 80, "mode": 100, "max": 140}}'
Premium tools (detect_anomaly,predict_forecast,analyze_risk,solve_constraints,analyze_graph,optimize_cmaes) need an API key or an x402 payment — seePricingbelow.
x402 (for autonomous agents):pay$0.001/callin USDC on Base — no signup, no API key. Send a signedPAYMENT-SIGNATUREheader on any premium endpoint; the API verifies, meters, and settles per call. Get a key instead with a one-linePOST /api/v1/auth/signup({"email":"you@…"}) — instant, no card.
We'd love to hear what you're working on. Share your use case, ask questions, or request features:
- Tell us what you're building
- Report an issue
- Join the conversation on Moltbook
- Live API:https://oraclaw-api.onrender.com
- Dashboard:https://web-olive-one-89.vercel.app
- npm:https://www.npmjs.com/org/oraclaw
- Demo:https://web-olive-one-89.vercel.app/demo
- GitHub:https://github.com/Whatsonyourmind/oraclaw
If this saved your agent from hallucinating math, star us :star:
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